The WGAN architecture constitutes a variant of generative models. It was introduced to increase learning stability and is less prone to mode collapse. Its structure ressembles the general GAN approach, except for its discriminator that learns on the basis of the Wasserstein distance by approximating a 1-Lipschitz function [1]}. Later, Arjosvky et al. made further adjustments to the weight clipping which improved the WGAN behaviour to better handle hyperparameter tuning [2]}. In addition, a comparative study increased the understanding of GAN behaviour regarding training stability and model saturation [3]}.
The authors of this article are aware that metaheuristics for hyperparameter optimization, e. g. swarm intelligence [4]} exist. However, this field of research is not subject to this research article.
